1. Field of the Invention
This invention relates to a pattern recognition processing system, and more particularly to a pattern recognition processing system in which characteristics of contours of a pattern to be recognized are extracted and respectively classified into groups and then the characteristics of each group are further classified for recognizing the pattern.
2. Description of the Prior Art
Generally, in the recognition of a pattern, the pattern is scanned so as to be divided into, for example, 30 .times. 30 meshes, the meshes are converted into binary-coded digital signals, and smoothing of pattern stroke, removal of an isolated point or like preprocessing is achieved by mask matching, and then characteristics of the pattern are extracted.
In the case of the recognition of a printed pattern, its characteristics can be easily extracted by mathematics. For the recognition of a handwritten pattern, however, it is necessary to find out invariable characteristics of the pattern by suppressing its variations inevitably resulting from handwriting. The pattern recognition is achieved based on strokes forming the pattern, or white areas formed by the pattern. In other words, the pattern recognition depends upon how faithfully the pattern seen by the naked eyes is reflected on recognition.
To this end, there have been proposed methods for recognizing a handwritten pattern by a reflection method, a trace method and a rolling method.
With the reflection method, the pattern is divided into circumscribed quadrangular areas and scanned along each row or in a horizontal direction to obtain represented segments between the circumscribed quadrangular frames (refer to 43 in FIG. 11A) and pattern strokes and between adjacent ones of the pattern strokes, and characteristics of the pattern are extracted based on the represented segments. The former represented segment is called the reflection segment (refer to 42 in FIG. 11A) and the latter represented segment is called the particular segment (refer to 44 in FIG. 11A). These represented segments are each formed by putting together points obtained by reflections when waves from the scanning starting positions (refer to 41 in FIG. 11A) strike against the pattern strokes (refer to the operations from three positions indicated by 41 in FIG. 11A). In this case, if points in the respective rows and columns are continuous, it is regarded as a characteristic of the pattern and if the points are discontinuous, it is not picked up as a pattern characteristic. Though differing with the ratio of the reflected position (refer to 1 in FIG. 11A) of the wave emitted from the starting point to the distance between adjacent pattern strokes, in the case of a steeply inclined pattern stroke, reflection segments are extracted, and in the case of a gently inclined pattern storke, they do not exist.
The particular segment is obtained in a similar manner.
Thus, the compressed white area present between the figure frame and the pattern stroke is represented by the reflection segment and the compressed white area present between the pattern strokes is represented by the particular segment, and clustering of the pattern is achieved by macroscopic characteristics obtained with the represented segments. Then, microscopic characteristics of the pattern obtained by the trace method and the rolling method are combined to determine an ultimate pattern.
The trace method is to extract parts which are invariable independently of variations in handprinting, i.e. endpoints, beinding points and cross-points.
The rolling method is to extract straight parts of the pattern stroke and clarify their interlinking relationship by making a round of the pattern stroke.
For the reflection method, refer to the following literature:
(i) Yoshida, Iwata and Yamamoto, "The Recognition of Hand-Written Katakana and Numeral" (Institute of Electronics and Communication Engineers of Japan, Research Data PRL 75-12 (1975- 6)) PA1 (ii) Yoshida, Iwata and Yamamoto, "The Recognition of 48 Handprinted Katakanas" (Institute of Electronics and Communication Engineers of Japan, Research Data PRL 75-9 (1975- 05) PA1 (iii) Yoshida, Iwata and Yamamoto, "The Recognition of 48 Handprinted Katakanas" (Institute of Electronics and Communication Engineers of Japan, Research Data PRL 75- 9 (1975-05) PA1 (iv) Kano, Toriwaki and Fukumura, "A Method for Convering Shaded Pattern into Linear Pattern" (Journal of Institute of Electronics and Communication Engineers of Japn 1972-10) PA1 (v) The Literature (i) PA1 (vi) E. C. Greanias, et al, "The Recognition of Hand-Written Numerals by Contour Analysis," IBM Journal (Jan. 1963) PA1 (vii) Tomita "The Recognition of Hand-Written Katakanas" (Journal of Institute of Electronics and Communication Engineers of Japan, 50, 4 (1967-4))
For the trace method, refer to the following literature:
For the rolling method, refer to the following literature:
As described above, in the prior art, the pattern is subjected to clustering based on the macroscopic information obtained by the reflection method, and then the pattern is recognized with the microscopic information obtained by another method. However, in the case of the number of categories of the pattern to be recognized being increased, recognition is difficult by the use of the above individual characteristic extraction methods. In this case, the kinds of figures increases as compared with those in the case of recognizing the pattern based on the individual characteristics, so that the amount of information to be extracted will naturally increase. Further, the difference in the shapes of figures becomes great and the number of similar figures also increases, so that, it is impossible to extract the characteristics of the pattern with the method of recognizing individual categories only.